Unsupervised Anomaly Detection for Hard Drives
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Abstract
In the age of smart
sensors and industry 4.0 continuous monitoring
of different machinery produce enormous
amount of data, because of that datacenters are now-a-days a very important asset
not only for large scale cloud
providers, but also for medium to large enterprises,
which decide to store in-house the
ever increasing data collected during business operations.
An efficient method for the maintenance of the great number of hard-drives
housed in datacenters is critical to assure avaiability in a cost effective manner.
Since 2013, Backblaze \url{https://www.backblaze.com/} has published statistics
and datasets for researchers to gain insights on hard drive performaces and
their failures, in this paper more than 2.5 million records,
following hard-drives S.M.A.R.T readings for over a year, will be analyzed.
The objective of this paper is to show that it is possible to build a completely
unsupervised pipeline which produces an anomaly score that highly
correlates to hard drives time to failure (TTF), in such a way a decision
to replace them can be made before failure, with minimal waste due to
false alarms. Favorable comparisons with state of the art supervised
classifiers will be presented.
A brief example of how such a pipeline can be
extended for data streams and continuos sensor monitoring will be given.
How to Cite
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predictive manteinance, data mining, unsupervised learning
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